An investigation of critical factors in medical device development through Bayesian networks

Authors:
Lourdes A. Medina;Marija Jankovic;Gül E. Okudan Kremer;Bernard Yannou
Affiliations:
Department of Industrial Engineering, University of Puerto Rico-Mayagüez, Mayagüez, PR, United States;Laboratoire Genie Industriel, Ecole Centrale Paris, Chatenay-Malabry, France;School of Engineering Design and Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, PA, United States;Laboratoire Genie Industriel, Ecole Centrale Paris, Chatenay-Malabry, France
Venue:
Expert Systems with Applications: An International Journal
Year:
2013

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Abstract

In this paper, we investigate the impact of product, company context and regulatory environment factors for their potential impact on medical device development (MDD). The presented work investigates the impact of these factors on the Food and Drug Administration's (FDA) decision time for submissions that request clearance, or approval to launch a medical device in the market. Our overall goal is to identify critical factors using historical data and rigorous techniques so that an expert system can be built to guide product developers to improve the efficiency of the MDD process, and thereby reduce associated costs. We employ a Bayesian network (BN) approach, a well-known machine learning method, to examine what the critical factors in the MDD context are. This analysis is performed using the data from 2400 FDA approved orthopedic devices that represent products from 474 different companies. Presented inferences are to be used as the backbone of an expert system specific to MDD.